3 papers
cs.LG2025
Universally Invariant Learning in Equivariant GNNs
Jiacheng Cen, Anyi Li, Ning Lin +5
Equivariant Graph Neural Networks (GNNs) have demonstrated significant success across various applications. To achieve completeness -- that is, the universal approximation property…
cs.LG2025
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Jiacheng Cen, Anyi Li, Ning Lin +3
Equivariant Graph Neural Networks (GNNs) that incorporate E(3) symmetry have achieved significant success in various scientific applications. As one of the most successful models,…
cs.LG2025
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Jiaqi Han, Jiacheng Cen, Liming Wu +13
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physi…